Analysis of de novo mutation from sequencing of related individuals and cells

通过相关个体和细胞的测序分析从头突变

基本信息

批准号：
9234033
负责人：
DONALD F. CONRAD
金额：
$ 50万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-08 至 2019-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9234033
关键词：
Address Age Aging Alleles Alpha Cell Animal Model Area Autistic Disorder Benchmarking Biological Cancer Biology Cancerous Cells Child Childhood Chromosome Segregation Communities Complex Computer software DNA Resequencing DNA Sequence Data Data Set Detection Development Developmental Delay Disorders Disease Dropout Drosophila genus Environmental Exposure Error Sources Experimental Models Family Genealogy Genes Genetic Genetic Research Genome Genomics Genotype Germ Cells Germ-Line Mutation Goals Heart Hereditary Disease High-Throughput Nucleotide Sequencing Human Human Characteristics Human Genetics Individual Investments Joints Malignant Neoplasms Medical Genetics Methodology Methods Microbe Modeling Mosaicism Mus Mutation Mutation Detection Noise Parents Phenotype Phylogeny Play Population Population Heterogeneity Probability Process Property Research Research Personnel Risk Factors Role Sampling Schizophrenia Scientist Site Somatic Mutation Statistical Methods Surveys Technology Testing Time Tissue Model Tissue Sample Tissues Toxicogenetics Trees Unmarried person Validation Variant age effect base clinical practice cost genetic pedigree genome-wide human disease human tissue improved insertion/deletion mutation insight markov model novel open source personalized genomic medicine practical application programs public health relevance rare variant sex single cell sequencing tool tumor whole genome

项目摘要

DESCRIPTION (provided by applicant): De novo DNA sequence mutations are mutations not inherited from a parent and play an important role in many human disorders, including cancer, autism, schizophrenia, and heart conditions. However, de novo mutations can be difficult to identify because sequencing errors are more common than mutations. Current approaches used to analyze DNA sequence data are inadequate to identify de novo mutations successfully at a genome scale because each potential de novo mutation must be validated by a costly and time-consuming validation process. Our goal is to improve the identification of de novo mutations in order to understand their role in genetic disorders. We will develop a novel statistical approach to identify de novo mutations, and we will implement it in software to make our method readily available to other researchers. Our first objective is to determine the probability that an apparent DNA sequence change is due to a de novo mutation, when analyzing short-read sequencing data from families. To determine this probability, we will integrate over other possible sources of error/noise including sequencing error, population diversity, and chromosome segregation. Secondly, we will expand on this model to detect somatic de novo mutations between multiple tissues from the same individual (e.g. matched tumor-normal datasets). Thirdly, we will develop new models to handle sequencing data from single-cell sequencing, which generates different probabilities of error compared to those discussed previously. These three aims are unified by a common approach of using genealogical information relating people, tissues, or individual cells, to improve the accuracy of de novo mutation discovery. Finally, we will implement these methods in an easy-to-use software package that will make the identification of de novo mutations possible for scientists working on subjects ranging from variation in mutation rates to the effects of aging. The methods developed here can benefit the hundreds, if not thousands, of studies that will search for and characterize de novo mutations in the coming decade. This package will be open source and free for the community to use.

描述（由申请人提供）：从头DNA序列突变是未从父母那里遗传而来的突变，而是在许多人类疾病中起重要作用，包括癌症，自闭症，精神分裂症和心脏病。但是，从头突变可能很难识别，因为测序误差比突变更常见。用于分析DNA序列数据的当前方法不足以在基因组量表上成功识别从头突变，因为每个潜在的从头突变都必须通过昂贵且耗时的验证过程来验证。我们的目标是改善从头突变的识别，以了解其在遗传疾病中的作用。我们将开发一种新型的统计方法来识别从头突变，并将其在软件中实施，以使我们的方法随时可用于其他研究人员。我们的第一个目的是确定在分析家族的短阅读测序数据时，明显的DNA序列变化是由于从头突变引起的。为了确定这种概率，我们将在其他可能的误差/噪声来源上整合，包括测序误差，人群多样性和染色体分离。其次，我们将在该模型上扩展，以检测来自同一个体的多个组织之间的从头突变（例如匹配的肿瘤正常数据集）。第三，我们将开发新的模型来处理单细胞测序的测序数据，该测序与前面讨论的误差相比会产生不同的错误概率。这三个目标是通过使用与人，组织或单个细胞相关的谱系信息来提高从头突变发现的准确性的一种常见方法来统一的。最后，我们将在易于使用的软件包中实施这些方法，该软件包将使从事从突变率变化到衰老的影响的科学家对从头突变的识别。这里开发的方法可以使数百种（即使不是数千个）的研究受益，这些研究将在未来十年中搜索和表征从头突变。此软件包将是开源的，可以免费使用社区。